com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess

Class MADecomposition

java.lang.Object

com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.MADecomposition

All Implemented Interfaces:

java.io.Serializable

public class MADecomposition
extends java.lang.Object
implements java.io.Serializable

This class decomposes a time series into the trend, seasonal and the stationary random components using the Moving Average Estimation with symmetric window.

That is,

 Xt = mt + st + Yt
 

We have

EY_t = 0 s_t+d = s_t Σ(s_j) = 0

The R equivalent function is decompose.

See Also:

"P. J. Brockwell and R. A. Davis, "p. 23. Chapter 1.4. Elimination of both Trend and Seasonality," in Time Series: Theory and Methods, 2nd ed. Springer, 2006.", Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
TimeSeries	random the stationary random component of the time series after the trend and seasonal components are removed
TimeSeries	seasonal the estimated seasonal effect of the time series
TimeSeries	trend the estimated trend of the time series

Constructor Summary

Constructors

Constructor and Description
MADecomposition(TimeSeries Xt, double[] MAFilter, int period) Decompose a time series into the trend, seasonal and the stationary random components using the Moving Average Estimation.
MADecomposition(TimeSeries Xt, int period) Decompose a periodic time series into the seasonal and stationary random components using no MA filter.
MADecomposition(TimeSeries Xt, int MAOrder, int period) Decompose a time series into the trend, seasonal and the stationary random components using the default filter.

Method Summary

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

trend

public final TimeSeries trend

the estimated trend of the time series

seasonal

public final TimeSeries seasonal

the estimated seasonal effect of the time series

random

public final TimeSeries random

the stationary random component of the time series after the trend and seasonal components are removed

Constructor Detail

MADecomposition

public MADecomposition(TimeSeries Xt,
                       double[] MAFilter,
                       int period)

Decompose a time series into the trend, seasonal and the stationary random components using the Moving Average Estimation.

Parameters:

Xt - a time series

MAFilter - the moving average filter to smooth the time series

period - the period of the time series; if aperiodic, use 1

MADecomposition

public MADecomposition(TimeSeries Xt,
                       int period)

Decompose a periodic time series into the seasonal and stationary random components using no MA filter.

Parameters:

Xt - a time series

period - the period of the time series; if aperiodic, use 0

MADecomposition

public MADecomposition(TimeSeries Xt,
                       int MAOrder,
                       int period)

Decompose a time series into the trend, seasonal and the stationary random components using the default filter.

Parameters:

Xt - a time series

MAOrder - the length of the MA filter (automatically increased by 1 for even MAOrder)

period - the period of the time series; if aperiodic, use 0

See Also:

"P. J. Brockwell and R. A. Davis, "Eq. 1.4.16. Chapter 1.4. Elimination of both Trend and Seasonality," in Time Series: Theory and Methods, 2nd ed. Springer, 2006."